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A Maneuver-Based Threat Assessment Strategy for Collision Avoidance

SAE International Journal of Passenger Cars - Electronic and Electrical Systems

Beihang University, China-Weiwen Deng
General Motors LLC, USA-Jinsong Wang
  • Journal Article
  • 07-12-01-0003
Published 2019-08-22 by SAE International in United States
Advanced driver-assistance systems (ADAS) are being developed for more and more complicated application scenarios, which often require more predictive strategies with better understanding of the driving environment. Taking traffic vehicles’ maneuvers into account can greatly expand the beforehand time span for danger awareness. This article presents a maneuver-based strategy to vehicle collision threat assessment. First, a maneuver-based trajectory prediction model (MTPM) is built, in which near-future trajectories of ego vehicle and traffic vehicles are estimated with the combination of vehicle’s maneuvers and kinematic models that correspond to every maneuver. The most probable maneuvers of ego vehicle and each traffic vehicles are modelled and inferred via Hidden Markov Models with mixture of Gaussians outputs (GMHMM). Based on the inferred maneuvers, trajectory sets consisting of vehicles’ position and motion states are predicted by kinematic models. Subsequently, time to collision (TTC) is calculated in a strategy of employing collision detection at every predicted trajectory instance. For this purpose, safe areas via bounding boxes are applied on every vehicle, and Separating Axis Theorem (SAT) is applied for collision prediction…
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Personalized Eco-Driving for Intelligent Electric Vehicles

Jilin University-Weiwen Deng, Jian Wu, Yaxin Li
Jilin University, ASCL-Bohua Sun, Rui He
Published 2018-08-07 by SAE International in United States
Minimum energy consumption with maximum comfort driving experience define the ideal human mobility. Recent technological advances in most Advanced Driver Assistance Systems (ADAS) on electric vehicles not only present a significant opportunity for automated eco-driving but also enhance the safety and comfort level. Understanding driving styles that make the systems more human-like or personalized for ADAS is the key to improve the system comfort. This research focuses on the personalized and green adaptive cruise control for intelligent electric vehicle, which is also known to be MyEco-ACC. MyEco-ACC is based on the optimization of regenerative braking and typical driving styles. Firstly, a driving style model is abstracted as a Hammerstein model and its key parameters vary with different driving styles. Secondly, the regenerative braking system characteristics for the electric vehicle equipped with 4-wheel hub motors are analyzed and braking force distribution strategy is designed. Finally, MyEco-ACC is constructed and optimized via theory of Nonlinear Model Prediction Control (NMPC). Regenerated energy is taken as the indicator for energy consumption and the key parameter in driving style model…
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Research on the Classification and Identification for Personalized Driving Styles

CATARC-Liang Xu
China FAW Co., Ltd.-Lei Fu
Published 2018-04-03 by SAE International in United States
Most of the Advanced Driver Assistance System (ADAS) applications are aiming at improving both driving safety and comfort. Understanding human drivers' driving styles that make the systems more human-like or personalized for ADAS is the key to improve the system performance, in particular, the acceptance and adaption of ADAS to human drivers. The research presented in this paper focuses on the classification and identification for personalized driving styles. To motivate and reflect the information of different driving styles at the most extent, two sets, which consist of six kinds of stimuli with stochastic disturbance for the leading vehicles are created on a real-time Driver-In-the-Loop Intelligent Simulation Platform (DILISP) with PanoSim-RT®, dSPACE® and DEWETRON® and field test with both RT3000 family and RT-Range respectively. Three physical quantities, the root mean square of vehicle acceleration, the time-to-start and the time gap of each driver, are extracted from test samples, and their mean and variance are used as clustering samples. Then driving styles are defined and classified into three categories via Particle Swarm Optimization Clustering (PSO-Clustering) algorithm. The…
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A Maneuver-Based Threat Assessment Strategy for Collision Avoidance

General Motors LLC-Jinsong Wang
Jilin University-Yaxin Li, Weiwen Deng, Bohua Sun, Jian Zhao
Published 2018-04-03 by SAE International in United States
Advanced driver assistance systems (ADAS) are being developed for more and more complicated application scenarios, which often require more predictive strategies with better understanding of driving environment. Taking traffic vehicles’ maneuvers into account can greatly expand the beforehand time span for danger awareness. This paper presents a maneuver-based strategy to vehicle collision threat assessment. First, a maneuver-based trajectory prediction model (MTPM) is built, in which near-future trajectories of ego vehicle and traffic vehicles are estimated with the combination of vehicle’s maneuvers and kinematic models that correspond to every maneuver. The most probable maneuvers of ego vehicle and each traffic vehicles are modeled and inferred via Hidden Markov Models with mixture of Gaussians outputs (GMHMM). Based on the inferred maneuvers, trajectory sets consisting of vehicles’ position and motion states are predicted by kinematic models. Subsequently, time to collision (TTC) is calculated in a strategy of employing collision detection at every predicted trajectory instance. For this purpose, safe areas via bounding boxes are applied on every vehicle, and Separating Axis Theorem (SAT) is applied for collision prediction,…
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Physical Modeling Method on Ultrasonic Sensors for Virtual Intelligent Driving

ASCL, Jilin University; Air Force Aviation University-Xin Li
ASCL, Jilin Unversity-Zhenyi Liu, Weiwen Deng, Yaxin Li
Published 2016-09-14 by SAE International in United States
Environmental sensing and perception is one of the key technologies on intelligent driving or autonomous vehicles. As a complementary part to current radar and lidar sensors, ultrasonic sensor has become more and more popular due to its high value to the cost. Different from other sensors mainly based on propagation of electromagnetic wave, ultrasonic sensor possesses some unique features and physical characteristics that bring many merits to autonomous vehicle research, like transparent obstacles and highly reflective surfaces detection. Its low-cost property can further bring down hardware cost to foster widespread use of intelligent driving or autonomous vehicles. To accelerate the development of autonomous vehicle, this paper proposes a high fidelity ultrasonic sensor model based on its physical characteristics, including obstacle detection, distance measurement and signal attenuation. Ultrasonic sensor model is simulated under PanoSim[1], a virtual driving environment and testing platform for vehicle intelligence technologies, which demonstrate that the proposed modeling method is effective and the developed model has sufficient fidelity.
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LiDAR Sensor Modeling for ADAS Applications under a Virtual Driving Environment

ASCL, Jilin University-Yaxin Li, Weiwen Deng, Zhenyi liu
ASCL, Jilin University; Air Force Aviation University-Xin Li
Published 2016-09-14 by SAE International in United States
LiDAR sensors have played more and more important role on Intelligent and Connected Vehicles (ICV) and Advanced Driver Assistance Systems (ADAS) .However, the development and testing of LiDAR sensors under real driving environment for ADAS applications are greatly limited by various factors, and often are impossible due to safety concerns. This paper proposed a novel functional LiDAR model under virtual driving environment to support development of LiDAR-based ADAS applications under early stage. Unlike traditional approaches on LiDAR sensor modeling, the proposed method includes both geometrical modeling approach and physical modeling approach. While geometric model mainly produces ideal scanning results based on computer graphics, the physical model further brings physical influences on top of the geometric model. The range detection is derived and optimized based on its physical detection and measurement mechanism. The model has been incorporated into PanoSim and proved to be valid and effective. A simulation of Autonomous Emergency Braking (AEB) feature is performed under PanoSim to verify the proposed model.
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